Monocular Human Pose Estimation. Industry and academia are looking at human pose estimation with increasing interest [1]}, [2]}, [3]}. Commercial solutions usually require special markers suite to track subjects from multiple point of views [4]}. Despite their satisfactory performances, these approaches are extremely costly and require careful setup choices to perform well at high speeds [5]}.
For these reasons, monocular approaches have been widely researched [6]}. Along with background, light conditions, texture, and image imperfection, monocular solutions must also handle the intrinsic ambiguity of monocular vision and therefore pose unanswered challenges to the research community. Model-based solutions are an established line of work on this problem. These approaches estimate the full 3D body and shape of the subjects by fitting a model of human body [7]}. Although recent model-based works achieve impressive performances [8]}, [9]}, [10]}, here we will focus on skeleton-based solutions. Skeleton-based approaches aim to regress 3D joints of the skeleton directly from images. As machine learning models deal with probability better than with scalar values, recent solutions predict dense probability maps (denominated heatmaps) of the location of the skeletal joints onto the image plane. In particular, Newell made a break-trough in the field by proposing Stacked-hourglass model [11]}. The authors stack multiple Convolutional Neural Networks to extract expressive heatmaps and apply a differentiable sort-argmax operator to retrieve the 2D pixel location of each joint.
Although we can adapt stacked-hourglass models to predict 3D (volumetric) heatmaps [12]}, this path is widely open for improvements, especially since Volumetric Heatmaps are computational and memory demanding [13]}.
Indeed, Mehta factorize volumentric heatmaps into three 2D heatmaps to lower computational costs [14]}. The authors train a deep learning model (VNect) to predict x, y, and z axes as dense 2D heatmaps and combine the predictions through triangulation. The computational and resources savings of VNect come with a price in terms of accuracy, as this method reaches higher Mean Per-Joint Precision Error (MPJPE) on common benchmarks.
Nibali develop this approach further and propose a model (Margipose) to predict \(xy\) , \(zy\) , and \(xz\)  heatmaps and regress the final 3D pose [15]}. Others advancements in 3D human pose estimation include GANs [16]} and temporal convolutions [17]}.
